conda-build | Commands and tools for building conda packages | Build Tool library

https://towardsdatascience.com/python-the-system-path-and-how-conda-and-pyenv-manipulate-it-234f8e8bbc3e

This article helped me debug this issue. I just had to make sure I deactivated out of conda environment completely even the (base) environment. For some reason even after deactivating from my po environment, it went to base environment

I ended up needing to go through an exception process in order to get past my company's firewall.

there are a few symptoms I would like to suggest looking into:

1. There is a WARNING in your error log SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. You have MANIFEST.in, setup.py and setup.cfg probably conflicting between them. Because setup.py is the build script for setuptools. It tells setuptools about your package (such as the name and version) as well as which code files to include. Also, An existing generated MANIFEST will be regenerated without sdist comparing its modification time to the one of MANIFEST.in or setup.py, as explained here.

Please refer to Building and Distributing Packages with Setuptools, also Configuring setup() using setup.cfg files and Quickstart for more information

1. Maybe not so important, but another thing worth looking into is the fact that there are 2 different python distributions being used at different stages, as Python 3.10 is used at: Using pip 22.0.2 from $PREFIX/lib/python3.10/site-packages/pip (python 3.10) (it is also in your conda dependencies) and Python 3.8 is used at: File "/Users/jespinoz/anaconda3/lib/python3.8/site-packages/conda_build/tarcheck.py", line 53, in info_files raise Exception('info/files') which is where the error happens. So maybe another configuration conflict related to this.

The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.

I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).

Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").

Try adjusting the meta.yaml to something like

If there are no .condarc sources, Conda falls back to the values that are hardcoded in the conda.base.context.Context class. Here's a relevant snippet:

• Conda executable in one api does not support all the features supported by conda in anaconda.

• Conda executable in one api can be used to download both intel optimized packages as well as anaconda packages.

• Conda executable in one api gives performance improvement for intel optimized packages.

• Since setvars is not sourced you are getting this warning

This solves fine (), but is indeed a complex solve mainly due to:

• underspecification
• lack of modularization

This particular environment specification ends up installing well over 300 packages. And there isn't a single one of those that are constrained by the specification. That is a huge SAT problem to solve and Conda will struggle with this. Mamba will help solve faster, but providing additional constraints can vastly reduce the solution space.

At minimum, specify a Python version (major.minor), such as python=3.9. This is the single most effective constraint.

Beyond that, putting minimum requirements on central packages (those that are dependencies of others) can help, such as minimum NumPy.

I assume the name "devenv" means this is a development environment. So, I get that one wants all these tools immediately at hand. However, Conda environment activation is so simple, and most IDE tooling these days (Spyder, VSCode, Jupyter) encourages separation of infrastructure and the execution kernel. Being more thoughtful about how environments (emphasis on the plural) are organized and work together, can go a long way in having a sustainable and painless data science workflow.

The environment at hand has multiple red flags in my book:

• conda-build should be in base and only in base
• snakemake should be in a dedicated environment
• notebook (i.e., Jupyter) should be in a dedicated environment, co-installed with nb_conda_kernels; all kernel environments need are ipykernel

I'd probably also have the linting/formatting packages separated, but that's less an issue. The real killer though is snakemake - it's just a massive piece of infrastructure and I'd strongly encourage keeping that separated.

Build tags in you environment.yml are quite strict requirements to satisfy and most often not needed. In your case, changing the yml file to

After a bit more digging (and carefully reading the CMake documentation on RPATH), the correct answer appears to be explicitly setting RPATH on installation. The relevant change to the linked gist is to add the following to the CMakeLists.txt after creating the targets (adapted from the linked Wiki):